‘Size’ and ‘Shape’ in the Measurement of Multivariate Proximity

Ordination and clustering methods are widely applied to ecological data that are non-negative (like species abundances or biomasses). These methods rely on a measure of multivariate proximity that quantifies differences between the sampling units (e.g. individuals, stations, time points), leading to results such as:

  1. Ordinations of the units, where interpoint distances optimally display the measured differences
  2. Clustering the units into homogeneous clusters
  3. Assessing differences between pre-specified groups of units (e.g. regions, periods, treatment–control groups)

In this video, Michael Greenacre introduces his new article, ‘‘Size’ and ‘Shape’ in the Measurement of Multivariate Proximity’, published in Methods in Ecology and Evolution, May 2017. In the context of species abundances, for example, he explains how much a chosen proximity measure captures the difference in “size” between two samples, i.e. difference in overall abundances, and differences in “shape”, i.e. differences in compositions or relative abundances.  He shows that the popular Bray-Curtis dissimilarity inevitably includes a part of the “size” difference in its measurement of multivariate proximity.

This video is based on the article ‘‘Size’ and ‘shape’ in the measurement of multivariate proximity‘ by Michael Greenacre.

The Right Tool for the Job: Using Zeta Diversity to Communicate Uncertainty in Ecological Modelling

Post provided by Mariona Roigé

The Need for Modelling

Green vegetable bug nymph (Nezara viridula). ©John Marris. Lincoln University.

Green vegetable bug nymph (Nezara viridula). ©John Marris. Lincoln University.

Despite how far modelling has taken us in science, the use of models remains controversial. Modelling covers a huge range of common practices, from scaled models of ships to determine the shape that will have the least resistance to water to complex, comprehensive ‘models of everything’. A great example of the latter is the Earth System Model. This model aims to understand the changes in global climate by taking into account the interaction between physical climate, biosphere, the atmosphere and the oceans. Basically, a model of how the Earth works.

The controversy in the use of modelling resides in how accurately the model describes reality and the level of confidence we have in its outputs. The first argument can be a bit counter-intuitive: sometimes, a very simple model can be a great predictor. Actually, the conventional view in ecology is that simple models are more generalisable than complex models, although this view is being challenged. However, the level of confidence, or the level of uncertainty, that we have in the outputs of the model is a crucial point. We need to be able to accurately determine our levels of uncertainty if we want people to trust our models. Continue reading

Assessment of Stream Health with DNA Metabarcoding

Following on from last week’s press release ‘How Clean are Finnish Rivers?’, Vasco Elbrecht et al. have produced a video to explain the methods in ‘Assessing strengths and weaknesses of DNA metabarcoding-based macroinvertebrate identification for routine stream monitoring‘.

In this video, the authors explore the potential of DNA metabarcoding to access stream health using macroinvertebrates. They compared DNA and morphology-based identification of bulk monitoring samples from 18 Finnish stream ecosystems. DNA-based methods show higher taxonomic resolution and similar assessment results as currently used morphology-based methods. Their study shows that the tested DNA-based methods integrate well with current approaches, but further optimisation and validation of DNA metabarcoding methods is encouraged.

This video is based on the article ‘Assessing strengths and weaknesses of DNA metabarcoding-based macroinvertebrate identification for routine stream monitoring‘ by Elbrecht et al.

 

Fast-Moving Biodiversity Assessment: Are We Already in the Future?

Post provided by Carola Gómez-Rodríguez & Alfried P. Vogler

Time flies… in the blink of an eye! And even more so in science. The molecular lab work we were used to two decades ago seems like ancient history to today’s PhD students. The speed of change in sequencing technology is so overwhelming that imagination usually fails to foresee how our daily work will be in 10 years’ time. But in the field of biodiversity assessment, we have very good clues. Next Generation Sequencing is quickly becoming our workhorse for ambitious projects of species and genetic inventories.

One by One Approach to Studying Biodiversity

For decades, most initiatives measured biodiversity in the same way: collect a sample of many individuals in the field, sort the specimens, identify them to a Linnaean species one at a time (if there was a good taxonomist in the group which, unfortunately, it is kind of lucky these days!), and count them. Or, if identification was based on molecular data, the specimen was subject to DNA extraction, to sequence one (or several) short DNA markers. This involved countless hours of work that could be saved if, instead of inventorying biodiversity specimen-by-specimen, we followed a sample-by-sample approach. To do this now, we just have to make a “biodiversity soup”.

Biodiversity assessment based on morphological identification and/or Sanger sequencing (“The one-by-one approach”)

Biodiversity assessment based on morphological identification and/or Sanger sequencing (“The one-by-one approach”)

Continue reading

How Clean are Finnish Rivers?

Below is a press release about the Methods paper ‘Assessing strengths and weaknesses of DNA metabarcoding-based macroinvertebrate identification for routine stream monitoring‘ taken from the University of Duisburg-Essen.

©Shanthanu Bhardwaj

©Shanthanu Bhardwaj

Dragonflies, mayflies and water beetles have one thing in common: They indicate how clean the streams are in which they live. Scientists from the University of Duisburg-Essen and the Finnish Environment Institute (SYKE) have developed a DNA-based method, which allows to assess the stream water quality with unprecedented speed and accuracy. The article – ‘Assessing strengths and weaknesses of DNA metabarcoding-based macroinvertebrate identification for routine stream monitoring‘ – was just released in the esteemed peer-reviewed journal Methods in Ecology and Evolution.

Traditional stream assessment using visual identification of indicator species is time-consuming, expensive and procedures are seldom standardised. Especially small organisms may look similar and misidentifications happen frequently. Using a genetic method to identify the species these concerns are not an issue, as even small organisms can be securely identified using a DNA marker. Continue reading

Editor Recommendation – HistMapR: Rapid Digitization of Historical Land-Use Maps in R

Post provided by Sarah Goslee

For an ecologist interested in long-term dynamics, one of the most thrilling experiences is discovering a legacy dataset stashed away somewhere.

For an ecologist interested in long-term dynamics, one of the most daunting experiences is figuring how to turn that box full of paper into usable data.

The new tool HistMapR, described in ’HistMapR: Rapid digitization of historical land-use maps in R’ by Alistair Auffret and colleagues, makes one part of that task much easier.

Examples of input (©Lantmäteriet) and output maps from (a–b) the District Economic map and (c–d) the Economic map.

Examples of input (©Lantmäteriet) and output maps from (a–b) the District Economic map and (c–d) the Economic map.

Historical maps with coloured areas denoting different land cover or use are a valuable record, but difficult to analyse. This R package automates much of the time-consuming and tedious process of turning paper maps into classified categorical raster maps.

A map is scanned, imported into R, and the software is trained by clicking in different areas of each category. It then automatically classifies pixels based on which colour they are most similar to. The resulting classification is assessed manually. The process can be repeated with slightly different parameters until a good fit is achieved.

The authors found 80-90% agreement between HistMapR classification and manual digitisation (sources of error included clarity of original maps and scan quality). Using HistMapR reduced the time needed for digitising a series of historical land cover maps from two months to two days. Ecologists interested in long-term dynamics should be cheering!

The HistMapR package is available on GitHub and you can find example scripts on Figshare, so you can get right to work.

HistMapR: Rapid digitization of historical land-use maps in R‘ by Auffret et al. is a freely available Applications article (no subscription required).

Estimating the Size of Animal Populations from Camera Trap Surveys

Below is a press release about the Methods paper ‘Distance sampling with camera traps‘ taken from the Max Planck Society.

A Maxwell's duiker photographed using a camera trap. Marie-Lyne Després-Einspenner

A Maxwell’s duiker photographed using a camera trap. ©Marie-Lyne Després-Einspenner

Camera traps are a useful means for researchers to observe the behaviour of animal populations in the wild or to assess biodiversity levels of remote locations like the tropical rain forest. Researchers from the University of St Andrews, the Max Planck Institute for Evolutionary Anthropology (MPI-EVA) and the German Centre for Integrative Biodiversity Research (iDiv) recently extended distance sampling analytical methods to accommodate data from camera traps. This new development allows abundances of multiple species to be estimated from camera trapping data collected over relatively short time intervals – information critical to effective wildlife management and conservation.

Remote motion-sensitive photography, or camera trapping, is revolutionising surveys of wild animal populations. Camera traps are an efficient means of detecting rare species, conducting species inventories and biodiversity assessments, estimating site occupancy, and observing behaviour. If individual animals can be identified from the images obtained, camera trapping data can also be used to estimate animal density and population size – information critical to effective wildlife management and conservation. Continue reading

Building Universal PCR Primers for Aquatic Ecosystem Assessments

Post provided by Vasco Elbrecht

Many things can negatively affect stream ecosystems – water abstraction, eutrophication and fine sediment influx are just a few. However, only intact freshwater ecosystems can sustainably deliver the ecosystem services – such as particle filtration, food biomass production and the supply of drinking water – that we rely on. Because of this, stream management and restoration has often been in the focus of environmental legislation world-wide. Macrozoobenthic communities are often key biological components of stream ecosystems. As many taxa within these communities are sensitive to negative stressors introduced by humans, they’re ideal for assessing the quality of water.

Unfortunately, most macrozoobenthic taxa – such as stone-, may-, and caddisflies as well as most other invertebrates – are often found in juvenile larval life stages in these ecosystems, so they’re often difficult to identify based on morphology. With the DNA based metabarcoding method though, almost all taxa in a stream can be reliably identified up to species level using a standardised gene fragment. One key component of this strategy is the development of universal markers, which allow detection of the diverse macrozoobenthic groups.

Our new R package PrimerMiner provides a framework for obtaining sequence data from available reference databases and identifying suitable primer binding sites for marker amplification. The package makes this process quicker and easier. In the following pictures, we summarise the key steps of DNA metabarcoding.

To find out more about PrimerMiner, read our Methods in Ecology and Evolution article ‘PrimerMiner: an r package for development and in silico validation of DNA metabarcoding primers’. Like all Applications articles, this paper is freely available to everyone.

What silver fir aDNA can tell us about Neolithic forests

Below is a press release about the Methods paper ‘HyRAD-X, a versatile method combining exome capture and RAD sequencing to extract genomic information from ancient DNA‘ taken from Swiss Federal Institute for Forest, Snow and Landscape Research WSL (this press release is also available in French, German and Italian via the links below).

A new technique makes it possible to cost-effectively analyse genetic material from fossil plants and animals. Researchers from the Swiss Federal Institute for Forest, Snow and Landscape Research WSL and the universities of Lausanne and Bern have used this technique to examine the DNA of silver fir remains found in lake sediment in Ticino. They found clues as to how forests reacted to the emergence of agriculture.

The new process utilises the latest advances in DNA technology to isolate ancient DNA (aDNA) from prehistoric plants and animals. The techniques used to date are, however, expensive. “As population geneticists often need several dozens samples to make reliable statements, many research ideas are not currently financially viable,” says Nadir Alvarez, a professor at the University of Lausanne’s Department of Ecology and Evolution.

The research team led by Alvarez and his colleagues Christoph Sperisen (a population geneticist at the WSL), Willy Tinner (a professor of palaeoecology at the University of Bern) and Sarah Schmid (a biologist from the University of Lausanne) has now developed a cost-effective alternative and demonstrated its potential with subfossil silver fir needles found at Origlio lake in Ticino. The team showcased the results in the research journal Methods in Ecology and Evolution. Continue reading

Digitizing Historical Land-use Maps with HistMapR

Habitat destruction and degradation represent serious threats to biodiversity, and quantification of land-use change over time is important for understanding the consequences of these changes to organisms and ecosystem service provision.

Historical land-use maps are important for documenting how habitat cover has changed over time, but digitizing these maps is a time consuming process. HistMapR is an R package designed to speed up the digitization process, and in this video we take an example map to show you how the method works.

Digitization is fast, and agreement with manually digitized maps of around 80–90% meets common targets for image classification. We hope that the ability to quickly classify large areas of historical land use will promote the inclusion of land-use change into analyses of biodiversity, species distributions and ecosystem services.

This video is based on the Applications article ‘HistMapR: Rapid digitization of historical land-use maps in R‘ by Auffret et al. This article is freely available to anyone (no subscription required).

The package is hosted on GitHub and example scripts can be downloaded from Figshare.